• Korean Journal of Food Science and Technology
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• v.33 no.1
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• pp.24-27
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• 2001

This study was performed to analyse aroma patterns of Nagaimo, Ichoimo and Tsukuneimo by the electronic nose with 32 conducting polymer sensors. Response by the electronic nose was analysed by the principal component analysis(PCA). Sensory evaluation also for organoleptic taste and odor of Nagaimo, Ichoimo and Tsukuneimo was performed. Nagaimo was very crunchy and sweet. Tsukuneimo was roasted nutty, hard, viscid taste and sticky. Ichoimo had intensive unique yam flavor and moderate hardness between Nagaimo and Ichoimo. Intensity of Ichoimo for unique yam flavor by the electronic nose was the strongest. The quality factor(QF) of PCA for normalized pattern by thirty two sensors showed less than 2, and so aroma pattern of three yam cultivars had no difference. But when the PCA was performed for normalized pattern by eight selected sensitive sensors, the QF for Nagaimo and Tsukuneimo is 2.057. Thus aroma pattern between Nagaimo and Tsukuneimo could be distinguished.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.576-579
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• 2009

The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. Therefore, it is more required to classify incoming e-mails efficiently and accurately. Currently, the e-mail classification techniques are focused on two way classification to filter spam mails from normal ones based mainly on Bayesian and Rule. The clustering method has been used for the multi-way classification of e-mails. But it has a disadvantage of low accuracy of classification. In this paper, we propose a novel multi-way e-mail classification method that uses PCA for automatic category generation and dynamic category hierarchy for high accuracy of classification. It classifies a huge amount of incoming e-mails automatically, efficiently, and accurately.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.9
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• pp.767-773
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• 2018

In many cases, modern aircraft are equipped with data acquisition system which checks the structural integrity of the aircraft. The analysis of the vibration data collected with the system is generally performed in dependence on a skilled expert who is familiar with aircraft design. Therefore, it is difficult to provide a representative and objective defect identification standard for general users. In this paper, we shows that it is possible to identify the type of maneuvers and faults by using the Principal Component Analysis(PCA) method in the vast vibration data collected during aircraft operation without using the existing aircraft design analysis. We classified the ROK Army aircraft vibration data for maneuvers and faults types, and applied the PCA to the classified data. Our result shows that it is possible to develop an objective maneuver/fault identification method without design analysis for general users.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.215-218
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• 2014

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.475-476
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• 2018

생물통계학에 기반한 사용자 인증의 새로운 방법으로 안구 운동 정보가 새롭게 각광받고 있다. 이 논문에서는 안구운동정보가 사용자 인증 문제에 왜 좋은 지를 설명하고, 인증의 정확도를 향상시키기 위한 방안으로 주성분분석에 의한 방법을 제안한다. 주성분 분석은 데이터에서 변동이 가장 큰 방향을 찾아주기에 이를 활용하여 안구운동 데이터의 특징을 추출하면 인증 성능이 향상될 수 있을 것이다.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.25-31
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• 2006

In this paper, This study proposes a method that use the whole face as features by using a color information and KLT that overcome the weak points of existing face extraction and face recognition. The significant information among the features of face is extracted by PCA which uses KLT. In this paper, you will find that the recognition efficiency is over 90% for the faces that have various size and angle by proposing the face recognition method using color information and the KLT.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1435-1444
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• 2006

We present an effective and real-time face detection method based on Principal Component Analysis(PCA) and Support Vector Machines(SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training data set in a significant way, so that it showed 90.1 % detection rates with a small quantity of training data. it can process 8 frames per second for $320{\times}240$ pixel images. This is an acceptable processing time for a real-time system.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.703-708
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• 2022

K-means clustering is a representative clustering technique. However, there is a limitation in not being able to integrate the performance evaluation scale and the method of determining the minimum number of clusters. In this paper, a method for numerically determining the minimum number of clusters is introduced. The explained variance is presented as an integrated measure. We propose that the k-means clustering method should be performed in the subspace of the PCA in order to simultaneously satisfy the minimum number of clusters and the threshold of the explained variance. It aims to present an explanation in principle why principal component analysis and k-means clustering are sequentially performed in pattern recognition and machine learning.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.314-317
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• 2003

기존의 침입탐지 시스템에서는 오용탐지모델이 널리 사용되고 있다. 이 모델은 낮은 오판율(False Alarm rates)을 가지고 있으나, 새로운 공격에 대해 전문가시스템(Expert Systems)에 의한 규칙추가를 필요로 한다. 그리고 그 규칙과 완전히 일치되는 시그너처만 공격으로 탐지하므로 변형된 공격을 탐지하지 못한다는 문제점을 가지고 있다 본 논문에서는 이러한 문제점을 보완하기 위해 주성분분석(Principle Component Analysis; 이하 PCA)과 서포트 벡터 머신(Support Vector Machines; 이하 SVM)을 이용한 침입탐지 시스템을 제안한다. 네트워크 상의 패킷은 PCA를 이용하여 결정된 주성분 공간에서 해석되고, 정상적인 흐름과 비정상적인 흐름에 대한 패킷이미지패턴으로 정규화 된다. 이러한 두 가지 클래스에 대한 SVM 분류기를 구현한다. 개발하는 침입탐지 시스템은 알려진 다양한 침입유형뿐만 아니라, 새로운 변종에 대해서도 분류기의 유연한 반응을 통하여 효과적으로 탐지할 수 있다.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.330-336
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• 2011

In this paper, we propose a new method to improve the performance of supervector linear kernel SVM (Support Vector Machine) for speaker identification. This method is based on splitting one training datum into several pieces of utterances. We use four different databases for evaluating performance and use PCA (Principal Component Analysis), GKPCA (Greedy Kernel PCA) and KMDA (Kernel Multimodal Discriminant Analysis) for feature enhancement. As a result, the proposed method shows improved performance for speaker identification using supervector linear kernel SVM.